Mobile plant operators

How to become an earthmoving and related plant operator

Official title: Earthmoving and related plant operators

Also called: Bulldozer operator, Excavator operator, Front-end loader operator, Pile-driver operator, Road-roller operator, Road surface laying machine operator, Snow-groomer operator

Earthmoving and related plant operators operate machines to excavate, grade, level, smooth and compact earth or similar materials.

Main tasks

Operating and monitoring excavating machinery equipped with moveable shovels, grab-buckets or dragline buckets, to excavate and move earth, rock, sand, gravel or similar materials
Operating and monitoring machinery for digging trenches for sewers, drainage, water, oil, gas or similar pipelines
Operating and monitoring machinery equipped with concave steel blades to move, distribute and level earth, sand, snow and other materials
Operating and monitoring equipment to remove sand, gravel and mud from bottom of body of water
Operating and monitoring machines for hammering wooden, concrete or steel piles into ground
Operating and monitoring power rollers to compact and smooth layers of materials in making roads and pavements, and similar work
Operating and monitoring machines which spread and smooth concrete or bituminous or tar preparations to construct roadways, roads or similar surfaces

Ways in

Licence or registration: Registering body: Construction Industry Council. Law: Construction Workers Registration Ordinance (建造業工人註冊條例), Cap. 583. Summary by Peernovo; check the body's current rules.

Pay · Outlook · AI and this job

Median pay: HK$20,000 a month
About 387,500 people in 2025, and about 403,200 projected by 2028. (+4.1%)
Generative AI can do relatively little of this work, by the ILO's global estimate.

Questions people ask

Do you need a degree to become an earthmoving and related plant operator?

Usually not. Only 2% of people in this job hold a bachelor's degree or higher.

Will AI change the work of an earthmoving and related plant operator?

Generative AI can do relatively little of this work, by the ILO's global estimate.

Sources

Census and Statistics Department (2021 Population Census): 2021 Population Census, Interactive Data Dissemination Service: working population by occupation (38 groups) and sex, age, education, income; median income and field of education (9 groups) (2021 Population Census), retrieved 4 October 2026
Census and Statistics Department (General Household Survey): Median monthly employment earnings by occupation (9 groups), excluding foreign domestic helpers (Table 210-06317A); employed persons by occupation, age and sex (210-06307A) and by industry (210-06308A) (2025 annual (series to 2026-07)), retrieved 4 October 2026
Labour and Welfare Bureau; tables published by C&SD: Mid-term Update of Manpower Projection to 2028: supplementary statistical tables (5 occupation groups, 17 selected industries) (2025 to 2028 (published 2026-09)), retrieved 4 October 2026
Census and Statistics Department: Average daily wages of workers engaged in public sector construction projects, 39 trades (Table 220-20001) (Monthly, 2021 series, 2026-07), retrieved 4 October 2026
International Labour Organization, Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, Gmyrek et al.) (ILO Working Paper 140, May 2025, Annex Table A1), CC BY 4.0, retrieved 4 October 2026

Updated 4 October 2026